Error in phylogenetic ordinal model with MCMCglmm()

Hi,

With three levels you have 4 cut-points, one of which needs estimating (call it cp.est)

cp<-c(-Inf, 0, cp.est, Inf)

If eta is the fixed+random effect linear predictor, Xb+Zu, then the probability of being in category i is

pnorm(cp[i+1], eta , sqrt(Vr)) - pnorm(cp[i], eta, sqrt(Vr))

when family=?threshold?. Vr is the residual (units) variance. When Vr=1 this is standard profit regression. When family=?ordinal? it is:

pnorm(cp[i+1], eta , sqrt(Vr+1)) - pnorm(cp[i], eta, sqrt(Vr+1))

Its not a big deal because the eta cam be rescaled to be equivalent:

eta_ordinal/sqrt(Vr+1) = eta_threshold/sqrt(Vr)

and Vr is fixed.

However, the MCMC algorithm for  family=?threshold? is more efficient.

Cheers,

Jarrod


On 3 Aug 2018, at 12:43, roee maor <roeemaor at gmail.com> wrote:

Thanks everyone for your quick and helpful responses.
Removing the 'trait' terms indeed solved this problem and I have taken
Jarrod's advice to use family="threshold" instead of "ordinal".

Would it be possible to get a quick summary of the main differences
between ordinal and threshold models? Both seem to be rarely used and
I can't find much documentation on their inner workings in the
MCMCglmm vignette, tutorial or course notes.

Cheers,
RoiOn Thu, 2 Aug 2018 at 14:45, Paul Buerkner <paul.buerkner at gmail.com> wrote:


If it doesn't end up working in MCMCglmm, you may also try the brms package.

See https://cran.r-project.org/web/packages/brms/vignettes/brms_phylogenetics.html for an introduction of
phylogenetic models in brms.

2018-08-02 15:39 GMT+02:00 Dexter Locke <dexter.locke at gmail.com>:


Maybe Response needs to be an ordered factor, not a factor with three levels. Try something like

valid$Response <- as.factor(valid$Response, ordered=T)

See also th clmm package.

HTH, Dexter



On Aug 2, 2018, at 9:24 AM, roee maor <roeemaor at gmail.com> wrote:

Dear list,

I'm using MCMCglmm to run a phylogenetic model where the response is a
3-level ordinal factor (i.e. level 2 is an intermediate phenotype
between 1 and 3), and the predictors include one factorial (foraging
habitat), one ordinal (trophic level), and several continuous
variables.

As far as I know MCMCglmm is the only package that can handle logistic
models for phylogenetically structured multi-level discrete data, but
please correct me if that's not the case.

My problem right now is that I can't get MCMCglmm() to work with the
'family' argument set to "ordinal", although it does work with
"categorical".
Here's the code I'm using:

packageVersion("MCMCglmm")

[1] ?2.25?

R.version.string

[1] "R version 3.4.3 (2017-11-30)"

## model specifications:

INphylo <- inverseA(mammaltree, nodes="ALL", scale=TRUE)  ## phylogeny with 1399 tips, setting nodes="TIPS" is extremely slow
k <- length(levels(valid$Response))
I <- diag(k-1)
J <- matrix(rep(1, (k-1)^2), c(k-1, k-1))


## categorical model (unordered response) - runs to completion

m1 <- MCMCglmm(Response ~ -1 + trait + ForagingHab + Troph_Lev + Mass + Mean.Diur.Range + Max.Temp.Warmest.M + Temp.Annual.Range + Precip.Driest.Month + PET,

+                random = ~ us(trait):Binomial,
+                rcov = ~ us(trait):units,
+                prior = list(R = list(fix=1, V=(1/k) * (I + J), n = k-1),
+                             G = list(G1 = list(V = diag(k-1), n = k-1))),
+                ginverse = list(Binomial=INphylo$Ainv),
+                burnin = 300000,
+                nitt = 3000000,
+                thin = 2000,
+                family = "categorical",
+                data = valid,
+                pl = TRUE)

## ordinal model and error message

m2 <- MCMCglmm(Response ~ -1 + trait + ForagingHab + Troph_Lev + Mass + Mean.Diur.Range + Max.Temp.Warmest.M + Temp.Annual.Range + Precip.Driest.Month + PET,

+                random = ~ us(trait):Binomial,
+                rcov = ~ us(trait):units,
+                prior = list(R = list(fix=1, V=1, n = k-1),
+                             G = list(G1 = list(V = diag(k-1), n = k-1))),
+                ginverse = list(Binomial=INphylo$Ainv),
+                burnin = 300000,
+                nitt = 3000000,
+                thin = 2000,
+                family = "ordinal",
+                data = valid,
+                pl = TRUE)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels

## the shape of the data

str(valid)

'data.frame': 1399 obs. of  16 variables:
$ Binomial           : chr  "Abrocoma_bennettii" "Abrothrix_andinus"
"Abrothrix_jelskii" "Abrothrix_longipilis" ...
$ Response           : Factor w/ 3 levels "1","2","3": 1 3 2 2 2 3 2 3 1 3 ...
$ ForagingHab        : Factor w/ 7 levels "1","3","4","5",..: 2 2 2 2
2 2 2 2 2 2 ...
$ Troph_Lev          : Factor w/ 3 levels "1","2","3": 1 2 2 2 2 3 2 1 1 1 ...
$ Mass               : num  250.5 24.9 34.5 38.9 24.5 ...
$ Annual.Mean.Temp   : num  12.42 7.26 9.18 9.9 8.62 ...
$ Mean.Diur.Range    : num  10.46 13.82 16.16 9.15 7.78 ...
$ Max.Temp.Warmest.M : num  22 16.6 19.1 19.8 17.2 ...
$ Min.Temp.Coldest.M : num  3.77 -3.98 -3.24 2.21 1.46 ...
$ Temp.Annual.Range  : num  18.3 20.6 22.4 17.6 15.7 ...
$ Mean.Temp.Warm.Q   : num  16 9.2 11.1 13.8 12.2 ...
$ Mean.Temp.Cold.Q   : num  8.87 4.49 6.39 5.98 4.87 ...
$ Annual.Precip      : num  166 645 558 903 1665 ...
$ Precip.Driest.Month: num  1.74 5.99 6.31 31.31 104.7 ...
$ AET                : num  213 482 704 455 361 ...
$ PET                : num  1074 1242 1305 677 638 ...


I don't understand what factors the error refers to, because there
sufficient levels in the response even if one is absorbed in the
intercept.

The R-constraint in the prior is specified as suggested in the
MCMCglmm tutorial (fix=1, V=1), but the error message is the same
whether I use this specification or the categorical model
specification (fix=1, V=(1/k)*(I + J)) .

On a side note - what parameters affect the acceptance rates? The
categorical models maintain a rate of around 0.3 so I think the mixing
could be improved.

Any input would be very much appreciated.

Many thanks,
Roi Maor